Postdoctoral Scholar - ML Emulator For Data Assimilation
The University of Chicago’s Department of Geophysical Sciences and AI for Climate Initiative (AICE) invite applications for a postdoctoral researcher to work with Prof Pedram Hassanzadeh to work at the interface of data assimilation and machine learning (ML) for global state analysis of the ocean and atmosphere, with a focus on biogeochemical cycle.
This postdoctoral researcher will focus on building an ML-based atmosphere-ocean coupled emulator using high-resolution simulation data, and then developing a data assimilation framework that is accelerated by the emulator. The work will start with idealized regional test cases and later extended to global scales and observational data.
The postdoc will perform research as part of the new multi-institution international project, InMOS (Integration of models and observations across scales), which aims to produce a robust global synthesis of the cycling, redistribution, and storage of carbon, oxygen, and heat in the ocean since pre‐industrial times. The goal is to quantify key ocean fluxes of global importance and shed light on the mechanisms driving ocean stressors such as acidification, warming, and deoxygenation.
The postdoc is expected to actively collaborate with the entire InMOS team, in particular, Profs Laure Zanna at NYU and Laure Resplandy at Princeton University.
The position, available immediately, is a full-time appointment for one year, with the possibility of renewal for up to three years, subject to satisfactory performance and available funding. Outstanding applicants with more than 3 years of postdoc experience will be considered for appointment as research scientists.
In addition to be a part of the InMOS, AICE, and the broader OBVI communities, the postdoc will benefit from major activities around AI and weather/climate at the University of Chicago, including the Data Science Institute, the Climate and Sustainable Growth Institute, and the AI+Science Initiative.
Required Qualifications
- Completion of a PhD in climate science, applied mathematics, Physics, engineering, computational science, geosciences, physical oceanography, or a related field at the time of the appointment;
- Strong programming and numerical skills;
- Experience with high-performance computing and analysis of large datasets;
- Ability to work independently and as part of an interdisciplinary team;
- Ability to work in a fast-paced environment;
- Strong communication skills.
Preferred Qualifications
- Experience with machine learning, particularly deep neural networks;
- Experience with data assimilation.
Application Process:
Interested candidates should submit:
1. A complete curriculum vitae that includes a list of publications and contact information of at least three references,
2. A statement (max 1 page) summarizing how their past research experience, expertise, and interests fit this position.
Please send these materials as a single PDF file to pedramh@uchicago.edu with the subject line "Application: InMOS". Applications will be reviewed on a rolling basis and accepted until June 1, 2025. The positions are available immediately. We encourage the applicants to submit their applications as soon as possible.
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